Platform Engineering Advisor (AI/ML Platform)

Quick Facts

• Role: Platform Engineering Advisor (AI/ML Platform)

• Focus: Cloud-based analytical frameworks and production GenAI/ML capabilities on Google Cloud

Description

As a Platform Engineering Advisor, you will develop and integrate technical frameworks deployed across private/public/hybrid cloud platforms to support scalable, performant analytical applications. You will identify patterns for data acquisition, processing, and management and collaborate with engineers and architects to build the foundation for a Data and Analytics Platform using BI/AI/ML. The work includes ML/GenAI model development, MLOps, deployment, observability, interpretability, and security/governance.

Responsibilities

• Build clean, modular, well-documented Python for machine learning, deep learning, and generative AI model development.

• Develop scalable data engineering and feature transformation workflows using BigQuery, Cloud Dataflow (Apache Beam), and GCS.

• Optimize inference latency/throughput and compute utilization on Google Cloud infrastructure.

• Design and maintain automated ML pipelines for extraction, training, hyperparameter tuning, evaluation, and deployment with Vertex AI Pipelines (Kubeflow Pipelines/TFX).

• Deploy models using Vertex AI Endpoints, Cloud Run, or GKE with containerized Python runtimes.

• Implement MLOps observability using Vertex AI Model Monitoring, Vertex ML Metadata, Cloud Logging, and Cloud Monitoring to detect drift, anomalous inputs, and latency regressions.

• Define operational readiness with SLOs and automated alerting.

• Collaborate to transition Python prototypes and Jupyter notebooks (Vertex AI Workbench) into production-grade microservices.

• Expose models via high-performance REST/gRPC APIs (e.g., FastAPI) and integrate into enterprise applications.

• Ensure transparency and interpretability using Vertex Explainable AI (feature attributions, Integrated Gradients, SHAP).

• Enforce security, compliance, and responsible AI governance using IAM, VPC Service Controls, and Secret Manager.

• Prototype emerging GenAI capabilities using Gemini models and Vertex AI tools (Model Garden, Agent Builder, fine-tuning).

Requirements

• 5–7 years of experience in Platform Engineering or related field; leadership in planning/consulting and senior multi-functional collaboration.

• Bachelor’s degree/equivalent in computer science, engineering, information systems, or equivalent training/work experience.

• Advanced Python capability (OOP, async, API development with FastAPI/Flask, packaging, pytest).

• Deep ML/deep learning expertise with PyTorch, TensorFlow, JAX, or Hugging Face Transformers.

• Experience building LLM applications (RAG pipelines and agentic workflows) using Vertex AI Studio/Model Garden (Gemini) and related services.

• Strong SQL and BigQuery (including BigQuery ML), plus Python data libraries (Pandas, Polars, PyArrow).

• Hands-on experience across Vertex AI lifecycle: training jobs, registry/endpoint hosting (online/batch/serverless), feature store/datasets, and pipelines/experiments.

• Experience building ETL/ELT pipelines with Cloud Dataflow (Apache Beam), Cloud Dataproc (PySpark), or Cloud Composer (Airflow).

• MLOps/engineering practices: CI/CD workflows for ML with Cloud Build and/or GitHub Actions/GitLab CI integrated with Artifact Registry.

• Containerization/deployment with Docker and Cloud Run/GKE.

• IaC experience with Terraform.

• Testing experience including unit/integration, load testing with Locust, and LLM evaluation benchmarks.

• Strong GCP architecture and security knowledge (networking, service accounts, IAM roles, data residency/security guardrails).

Benefits

Comprehensive benefits (details provided during application).